{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:01:28Z","timestamp":1786114888170,"version":"build-2736575974"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004219","name":"Paul Scherrer Institut","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471297"],"award-info":[{"award-number":["62471297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.media.2026.104210","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:01:44Z","timestamp":1783782104000},"page":"104210","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Contrastive Discrepancy: A label-free metric for deformable image registration supporting testing-time hyperparameter selection"],"prefix":"10.1016","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2284-1700","authenticated-orcid":false,"given":"Xia","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4575-0026","authenticated-orcid":false,"given":"Jihe","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3987-9270","authenticated-orcid":false,"given":"Weijie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8550-3767","authenticated-orcid":false,"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7410-9832","authenticated-orcid":false,"given":"Jiquan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1090-2844","authenticated-orcid":false,"given":"Xixin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6613-7101","authenticated-orcid":false,"given":"Joachim M.","family":"Buhmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6444-6894","authenticated-orcid":false,"given":"Jianqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1107","key":"10.1016\/j.media.2026.104210_b1","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20190594","article-title":"Online daily adaptive proton therapy","volume":"93","author":"Albertini","year":"2020","journal-title":"Br. J. Radiol."},{"issue":"10","key":"10.1016\/j.media.2026.104210_b2","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abf8f5","article-title":"An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer","volume":"66","author":"Amstutz","year":"2021","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"10.1016\/j.media.2026.104210_b3","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","article-title":"Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain","volume":"12","author":"Avants","year":"2008","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104210_b4","series-title":"2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom)","first-page":"1723","article-title":"Ternary search algorithm: Improvement of binary search","author":"Bajwa","year":"2015"},{"issue":"8","key":"10.1016\/j.media.2026.104210_b5","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","article-title":"Voxelmorph: a learning framework for deformable medical image registration","volume":"38","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.media.2026.104210_b6","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1002\/jmrs.417","article-title":"Deforming to best practice: key considerations for deformable image registration in radiotherapy","volume":"67","author":"Barber","year":"2020","journal-title":"J. Med. Radiat. Sci."},{"key":"10.1016\/j.media.2026.104210_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.phro.2024.100647","article-title":"Tools and recommendations for commissioning and quality assurance of deformable image registration in radiotherapy","author":"Bosma","year":"2024","journal-title":"Phys. Imaging. Radiat. Oncol."},{"issue":"1","key":"10.1016\/j.media.2026.104210_b8","first-page":"32","article-title":"3D variation in delineation of head and neck organs at risk","volume":"7","author":"Brouwer","year":"2012","journal-title":"Radiother. Oncol."},{"issue":"23","key":"10.1016\/j.media.2026.104210_b9","doi-asserted-by":"crossref","DOI":"10.3390\/app142310890","article-title":"Anatomical plausibility in deformable image registration using Bayesian optimization for brain MRI analysis","volume":"14","author":"Casta\u00f1o-Aguirre","year":"2024","journal-title":"Appl. Sci.","ISSN":"https:\/\/id.crossref.org\/issn\/2076-3417","issn-type":"print"},{"issue":"7","key":"10.1016\/j.media.2026.104210_b10","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1088\/0031-9155\/54\/7\/001","article-title":"A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets","volume":"54","author":"Castillo","year":"2009","journal-title":"Phys. Med. Biol."},{"issue":"10","key":"10.1016\/j.media.2026.104210_b11","doi-asserted-by":"crossref","first-page":"5935","DOI":"10.1002\/mp.15163","article-title":"Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy","volume":"48","author":"Cazoulat","year":"2021","journal-title":"Med. phys."},{"key":"10.1016\/j.media.2026.104210_b12","series-title":"IEEE Int. Symp. Biomed. Imaging","first-page":"1","article-title":"Uncertainty-aware test-time adaptation for inverse consistent diffeomorphic lung image registration","author":"Chaudhary","year":"2025"},{"key":"10.1016\/j.media.2026.104210_b13","series-title":"Int. Conf. Med. Image Comput. Comput. Assist. Interv.","first-page":"46","article-title":"A deep discontinuity-preserving image registration network","author":"Chen","year":"2021"},{"issue":"23","key":"10.1016\/j.media.2026.104210_b14","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abc363","article-title":"Deep learning-based medical image segmentation with limited labels","volume":"65","author":"Chi","year":"2020","journal-title":"Phys. Med. Biol."},{"issue":"7","key":"10.1016\/j.media.2026.104210_b15","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/42.932742","article-title":"Consistent image registration","volume":"20","author":"Christensen","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b16","first-page":"54819","article-title":"Lung250M-4B: a combined 3D dataset for CT-and point cloud-based intra-patient lung registration","volume":"36","author":"Falta","year":"2023","journal-title":"NeurIPS"},{"issue":"1","key":"10.1016\/j.media.2026.104210_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/neco.1992.4.1.1","article-title":"Neural networks and the bias\/variance dilemma","volume":"4","author":"Geman","year":"1992","journal-title":"Neural Comput."},{"key":"10.1016\/j.media.2026.104210_b18","series-title":"Multi-objective learning for deformable image registration","author":"Grewal","year":"2024"},{"key":"10.1016\/j.media.2026.104210_b19","series-title":"cIDIR: Conditioned implicit neural representation for regularized deformable image registration","author":"Hadramy","year":"2025"},{"key":"10.1016\/j.media.2026.104210_b20","series-title":"The elements of statistical learning: data mining, inference, and prediction","author":"Hastie","year":"2009"},{"key":"10.1016\/j.media.2026.104210_b21","series-title":"Inform. Process. Med. Imaging","first-page":"3","article-title":"Hypermorph: Amortized hyperparameter learning for image registration","author":"Hoopes","year":"2021"},{"key":"10.1016\/j.media.2026.104210_b22","article-title":"Learning the effect of registration hyperparameters with hypermorph","volume":"1","author":"Hoopes","year":"2022","journal-title":"J. Mach. Learn. Biomed. Imaging"},{"issue":"1122","key":"10.1016\/j.media.2026.104210_b23","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20210001","article-title":"Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy\u2013the need for guidance and QA tools","volume":"94","author":"Hussein","year":"2021","journal-title":"Br. J. Radiol."},{"key":"10.1016\/j.media.2026.104210_b24","first-page":"108331","article-title":"Deep learning in medical image registration: Magic or mirage?","volume":"37","author":"Jena","year":"2024","journal-title":"NeurIPS"},{"issue":"12","key":"10.1016\/j.media.2026.104210_b25","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.3390\/bioengineering10121340","article-title":"Interobserver agreement in automatic segmentation annotation of prostate magnetic resonance imaging","volume":"10","author":"Jin","year":"2023","journal-title":"Bioengineering"},{"issue":"1","key":"10.1016\/j.media.2026.104210_b26","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","article-title":"Elastix: a toolbox for intensity-based medical image registration","volume":"29","author":"Klein","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b27","first-page":"15","article-title":"Fast template matching","volume":"vol. 95","author":"Lewis","year":"1995"},{"key":"10.1016\/j.media.2026.104210_b28","article-title":"Gaussian primitives for deformable image registration","volume":"35","author":"Li","year":"2025","journal-title":"Phys. Imaging. Radiat. Oncol."},{"issue":"1","key":"10.1016\/j.media.2026.104210_b29","doi-asserted-by":"crossref","first-page":"e70248","DOI":"10.1002\/mp.70248","article-title":"Continuous spatial-temporal deformable image registration and 4d frame interpolation","volume":"53","author":"Li","year":"2026","journal-title":"Medical Physics"},{"key":"10.1016\/j.media.2026.104210_b30","unstructured":"Li, X., Zhang, F., Li, M., Weber, D., Lomax, A., Buhmann, J., Zhang, Y., 2024. Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction. In: Int. Conf. Use Comput. Radiat. Ther.."},{"issue":"2","key":"10.1016\/j.media.2026.104210_b31","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/42.563664","article-title":"Multimodality image registration by maximization of mutual information","volume":"16","author":"Maes","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b32","series-title":"Int. Conf. Med. Image Comput. Comput. Assist. Interv.","first-page":"35","article-title":"Conditional deformable image registration with convolutional neural network","author":"Mok","year":"2021"},{"issue":"10","key":"10.1016\/j.media.2026.104210_b33","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1118\/1.2775667","article-title":"The management of imaging dose during image-guided radiotherapy: report of the AAPM task group 75","volume":"34","author":"Murphy","year":"2007","journal-title":"Med. phys."},{"issue":"11","key":"10.1016\/j.media.2026.104210_b34","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/TMI.2010.2053043","article-title":"Intensity-based image registration by minimizing residual complexity","volume":"29","author":"Myronenko","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b35","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.radonc.2020.04.046","article-title":"Deformable image registration uncertainty for inter-fractional dose accumulation of lung cancer proton therapy","volume":"147","author":"Nenoff","year":"2020","journal-title":"Radiother. Oncol."},{"issue":"2","key":"10.1016\/j.media.2026.104210_b36","first-page":"101","article-title":"Deformable image registration in radiation therapy","volume":"35","author":"Oh","year":"2017","journal-title":"Radiother. Oncol."},{"issue":"2","key":"10.1016\/j.media.2026.104210_b37","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1088\/0031-9155\/58\/2\/287","article-title":"Scale invariant feature transform in adaptive radiation therapy: a tool for deformable image registration assessment and re-planning indication","volume":"58","author":"Paganelli","year":"2012","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"10.1016\/j.media.2026.104210_b38","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.radonc.2018.03.001","article-title":"Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours","volume":"128","author":"Ribeiro","year":"2018","journal-title":"Radiother. Oncol."},{"issue":"6","key":"10.1016\/j.media.2026.104210_b39","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/TMI.2003.814791","article-title":"Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint","volume":"22","author":"Rohlfing","year":"2003","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b40","series-title":"The nature of statistical learning theory","author":"Sain","year":"1996"},{"key":"10.1016\/j.media.2026.104210_b41","article-title":"Plastimatch: an open source software suite for radiotherapy image processing","volume":"vol. 3","author":"Sharp","year":"2010"},{"issue":"5","key":"10.1016\/j.media.2026.104210_b42","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.prro.2021.02.012","article-title":"Benchmarking of deformable image registration for multiple anatomic sites using digital data sets with ground-truth deformation vector fields","volume":"11","author":"Shi","year":"2021","journal-title":"Pract. Radiat. Oncol."},{"key":"10.1016\/j.media.2026.104210_b43","doi-asserted-by":"crossref","first-page":"686","DOI":"10.59275\/j.melba.2024-d434","article-title":"HyperPredict: Estimating hyperparameter effects for instance-specific regularization in deformable image registration","volume":"2","author":"Shuaibu","year":"2024","journal-title":"Mach. Learn. Biomed. Imaging"},{"key":"10.1016\/j.media.2026.104210_b44","series-title":"Int. Conf. Med. Image Comput. Comput. Assist. Interv.","first-page":"174","article-title":"Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021","author":"Siebert","year":"2021"},{"key":"10.1016\/j.media.2026.104210_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103249","article-title":"Medical image registration via neural fields","volume":"97","author":"Sun","year":"2024","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.media.2026.104210_b46","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TMI.2023.3321425","article-title":"Robust deformable image registration using cycle-consistent implicit representations","volume":"43","author":"Van Harten","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.media.2026.104210_b47","first-page":"137","article-title":"Alignment by maximization of mutual information","volume":"24","author":"Viola","year":"1997","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.media.2026.104210_b48","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1109\/TMI.2016.2610583","article-title":"Isotropic total variation regularization of displacements in parametric image registration","volume":"36","author":"Vishnevskiy","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b49","series-title":"Framing image registration as a landmark detection problem for label-noise-aware task representation (hitr)","author":"Waldmannstetter","year":"2023"},{"issue":"4","key":"10.1016\/j.media.2026.104210_b50","first-page":"600","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104210_b51","series-title":"Int. MICCAI Brainlesion Workshop","first-page":"178","article-title":"Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks","author":"Wang","year":"2017"},{"key":"10.1016\/j.media.2026.104210_b52","series-title":"Int. Conf. Med. Imaging Deep Learn.","first-page":"1349","article-title":"Implicit neural representations for deformable image registration","author":"Wolterink","year":"2022"},{"key":"10.1016\/j.media.2026.104210_b53","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ejmp.2022.01.006","article-title":"A feasible method to evaluate deformable image registration with deep learning\u2013based segmentation","volume":"95","author":"Yang","year":"2022","journal-title":"Phys. Med."},{"issue":"1","key":"10.1016\/j.media.2026.104210_b54","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2478\/raon-2025-0006","article-title":"Interobserver and sequence variability in the delineation of pelvic organs at risk on magnetic resonance images","volume":"59","author":"Zheng","year":"2025","journal-title":"Radiother. Oncol."},{"issue":"3","key":"10.1016\/j.media.2026.104210_b55","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1118\/1.3302141","article-title":"Analysis of deformable image registration accuracy using computational modeling","volume":"37","author":"Zhong","year":"2010","journal-title":"Med. phys."},{"issue":"2","key":"10.1016\/j.media.2026.104210_b56","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","article-title":"Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports","volume":"11","author":"Zou","year":"2004","journal-title":"Acad. Radiol."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526002793?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526002793?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T22:46:09Z","timestamp":1785969969000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526002793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":56,"alternative-id":["S1361841526002793"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104210","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Contrastive Discrepancy: A label-free metric for deformable image registration supporting testing-time hyperparameter selection","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104210","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104210"}}